Benchmark: Reduce vs map/join testaaaaa

Script Preparation code:

var items = [];
for (var i=0; i<1000; i++) {
  items[i] = { id: i } 
}

Tests:

reduce
items.reduce((prev, curr) => prev += '-' + curr, '')
map join
items.map(i => i.name).join('-')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map join

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**What is being tested?**

The provided benchmark tests two approaches to concatenate strings: `reduce()` and `map()`/`join()`. The test creates an array of 1000 objects, each with an `id` property. The purpose of this setup is to simulate a scenario where you need to concatenate multiple strings together.

**Options compared:**

The benchmark compares the performance of two approaches:

1. **Reduce():` The `reduce()` method applies a callback function to each element in the array and reduces the result to a single value. In this case, it concatenates all the strings together.
2. **Map() + Join():` This approach uses the `map()` method to create a new array with transformed values (in this case, just the `name` property of each object), and then joins the resulting strings together using the `join()` method.

**Pros and Cons:**

*   **Reduce():`
    *   Pros:
        *   Simple and concise code.
        *   Can be more efficient for small arrays or simple concatenations.
    *   Cons:
        *   Requires an initial value for the accumulator, which can be a hassle to set up.
        *   May not be as readable as other approaches.
*   **Map() + Join():`
    *   Pros:
        *   More flexible and reusable than `reduce()` (e.g., you can map over different arrays).
        *   Can handle more complex transformations (e.g., multiple concatenations or conditional logic).
    *   Cons:
        *   Requires creating an intermediate array, which can be memory-intensive.
        *   May have performance overhead due to the extra step of mapping and joining.

**Library usage:**

The `reduce()` method is a built-in JavaScript function. No additional libraries are required for this test case.

**Special JS feature or syntax:**

There is no special JS feature or syntax used in this benchmark. It's a straightforward example of using these two methods to concatenate strings.

Now, let's talk about other alternatives:

*   **String concatenation with the `+` operator:** This approach would involve using string literals and chaining together multiple `+` operators to concatenate the values. While it works, it's generally considered less efficient and less readable than the approaches tested here.
*   **Using a template literal (backticks):** A modern JavaScript feature that allows you to embed expressions inside backticks (`) to create template literals. This approach can be more readable and expressive but may not have significant performance benefits over the methods tested in this benchmark.

In summary, MeasureThat.net provides a simple yet informative way for developers to test and compare the performance of different approaches to string concatenation in JavaScript. By understanding the pros and cons of each method, you can choose the most suitable approach for your specific use case.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Linux

View result in a separate tab

Test name	Executions per second
reduce	23835.6 Ops/sec
map join	141456.8 Ops/sec

Related benchmarks:

Reduce vs map/join testaaaaa (version: 0)

Comparing performance of: reduce vs map join

Created: 8 months ago by: Guest

Jump to the latest result

reduce

map join

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):